GRNN的数据预测-基于广义回归神经网络

%% 案例8：GRNN的数据预测—基于广义回归神经网络的货运量预测

%% 清空环境变量

clc;

clear all

close all

nntwarn off;

%% 载入数据

load data;

% 载入数据并将数据分成训练和预测两类

p_train=p(1:12,:);

t_train=t(1:12,:);

p_test=p(13,:);

t_test=t(13,:);

%% 交叉验证

desired_spread=[];

mse_max=10e20;

desired_input=[];

desired_output=[];

result_perfp=[];

indices = crossvalind('Kfold',length(p_train),4);

h=waitbar(0,'正在寻找最优化参数....')

k=1;

for i = 1:4

perfp=[];

disp(['以下为第',num2str(i),'次交叉验证结果'])

test = (indices == i); train = ~test;

p_cv_train=p_train(train,:);

t_cv_train=t_train(train,:);

p_cv_test=p_train(test,:);

t_cv_test=t_train(test,:);

p_cv_train=p_cv_train';

t_cv_train=t_cv_train';

p_cv_test= p_cv_test';

t_cv_test= t_cv_test';

[p_cv_train,minp,maxp,t_cv_train,mint,maxt]=premnmx(p_cv_train,t_cv_tra in);

p_cv_test=tramnmx(p_cv_test,minp,maxp);

for spread=0.1:0.1:2;

net=newgrnn(p_cv_train,t_cv_train,spread);

waitbar(k/80,h);

disp(['当前spread值为', num2str(spread)]);

test_Out=sim(net,p_cv_test);

test_Out=postmnmx(test_Out,mint,maxt);

error=t_cv_test-test_Out;

disp(['当前网络的mse为',num2str(mse(error))])

perfp=[perfp mse(error)];

if mse(error)

mse_max=mse(error);

desired_spread=spread;

desired_input=p_cv_train;

desired_output=t_cv_train;

end

k=k+1;

end

result_perfp(i,:)=perfp;

end;

close(h)

disp(['最佳spread值为',num2str(desired_spread)])

disp(['此时最佳输入值为'])

desired_input

disp(['此时最佳输出值为'])

desired_output

%% 采用最佳方法建立GRNN网络

net=newgrnn(desired_input,desired_output,desired_spread);

p_test=p_test';

p_test=tramnmx(p_test,minp,maxp);

grnn_prediction_result=sim(net,p_test);

grnn_prediction_result=postmnmx(grnn_prediction_result,mint,maxt);

grnn_error=t_test-grnn_prediction_result';

disp(['GRNN神经网络三项流量预测的误差为',num2str(abs(grnn_error))])

save best desired_input desired_output p_test t_test grnn_error mint maxt